Subject-Adaptive Loose-Fitting Smart Garment Platform for Human Activity Recognition

The ability to recognize and detect changes in human posture is important in a wide range of applications such as health care and human-computer-interaction. Achieving this goal using loose-fit garments instrumented with sensors is particularly challenging, due to the complex interaction between gar...

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Bibliographic Details
Published inACM transactions on sensor networks Vol. 19; no. 4; pp. 1 - 23
Main Authors Lin, Qi, Peng, Shuhua, Wu, Yuezhong, Liu, Jun, Jia, Hong, Hu, Wen, Hassan, Mahbub, Seneviratne, Aruna, Wang, Chun H
Format Journal Article
LanguageEnglish
Published New York, NY ACM 30.11.2023
Subjects
CNN-LSTM
Computing methodologies
domain adaptation
Human-centered computing
Machine learning
smart garment
Strain sensor
Ubiquitous and mobile computing
smart garment
domain adaptation
Strain sensor
CNN-LSTM
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Summary:The ability to recognize and detect changes in human posture is important in a wide range of applications such as health care and human-computer-interaction. Achieving this goal using loose-fit garments instrumented with sensors is particularly challenging, due to the complex interaction between garments and human body. Herein, we present a method to detect and recognize human posture with casual loose-fitting smart garments integrated with highly sensitive, stretchable, optical transparent and low-cost strain sensors. By attaching these sensors to an off-the-shelf casual jacket, we developed a smart loose-fitting sensing garment, which enables posture recognition using a deep learning model, domain-adaptive CNN-LSTM. This deep learning model overcame the noise and variation due to the complex interaction between loose-fitting garments and human body. Considering that users’ labeled data are usually not available in the training stage, an additional domain discriminator path on the conventional CNN-LSTM model has been introduced to further improve the adaptability. To evaluate the potential of this loose-fitting smart garment, three case studies were conducted under realistic conditions: recognitions of human activities, stationary postures with random hand movements and slouch. Our results demonstrate the potential of the proposed smart garment system for practical applications.
ISSN:1550-4859
1550-4867
1550-4867
DOI:10.1145/3584986